1. Field of the Invention
The present invention is related to knowledge management systems and more particularly to efficiently allocating expert resources in knowledge management systems.
2. Background Description
As the knowledge economy has grown, people are seeking information to assist with their jobs, personal lives or with life-long learning. However, while more information has is available, it is also more widely distributed, e.g., geographically, across businesses, and/or across a number of experts specializing in different areas. A number of approaches have been used to connect people seeking information with experts and/or educational services. For example, typical state of the art search engines produce a hitlist ranking the information objects matching a query from best match to poorest match and, hopefully, ranking the hits as to their match to the query. State of the art answering services attempt to provide succinct answers (usually only one) to a specific question based on linguistic analysis; here the incoming queries tend to be full sentences. These answering services provide a more specific match, matching semantic or grammatical structure in the question to other semantic or grammatical structures in information objects. State of the art “expertise location” systems automatically identify an expert in a given field that can answer queries in that field, e.g., the IBM Lotus Knowledge Discovery Engine. Also, many state of the art help desks include an automatic queuing system that directs queries to appropriate queues or experts, typically, matching the content of the query to an expert profile.
Unfortunately, typically such state of the art approaches either connect information seekers with experts manually or, are not very selective in matching experts. Currently, only very general factors are considered in automatically matching queries and experts and, therefore, providing a very general pairing, but certainly not the best match possible. Worse still, these approaches frequently have resulted in skill mismatches between the requester and answering expert, e.g., where the educational level of one is much higher than the other. So, for example, a freshman may ask a standard textbook question that is fielded by full professor at a college doing advanced research in physics. An upper-classman or a graduate student would suffice for this type of question but a full professor is overkill.
Thus, there is a need for an efficient allocation of expert resources in knowledge management systems.